Heess, Nicolas

94 publications

AISTATS 2025 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

ICML 2025 EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control Samuel Holt, Todor Davchev, Dhruva Tirumala, Ben Moran, Atil Iscen, Antoine Laurens, Yixin Lin, Erik Frey, Markus Wulfmeier, Francesco Romano, Nicolas Heess

ICLR 2025 Learning from Negative Feedback, or Positive Feedback or Both Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Michael Bloesch, Rishabh Joshi, Thomas Lampe, Junhyuk Oh, Nicolas Heess, Jonas Buchli, Martin Riedmiller

ICML 2025 Learning-Order Autoregressive Models with Application to Molecular Graph Generation Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis Titsias

ICLR 2025 Re-Evaluating Open-Ended Evaluation of Large Language Models Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras, Nicolas Heess, Marc Lanctot

NeurIPSW 2024 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

ICML 2024 Genie: Generative Interactive Environments Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Maria Elisabeth Bechtle, Feryal Behbahani, Stephanie C.Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando De Freitas, Satinder Singh, Tim Rocktäschel

CoRL 2024 Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin Riedmiller, Nicolas Heess

ICLR 2024 NfgTransformer: Equivariant Representation Learning for Normal-Form Games Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess

ICML 2024 Offline Actor-Critic Reinforcement Learning Scales to Large Models Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Maria Elisabeth Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin Riedmiller

ICML 2024 PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter

ICLR 2024 Replay Across Experiments: A Natural Extension of Off-Policy RL Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin Riedmiller, Nicolas Heess, Markus Wulfmeier

TMLR 2024 RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauza Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott Reed, Sergio Gómez Colmenarejo, Jonathan Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, Jose Enrique Chen, Yusuf Aytar, David Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

NeurIPS 2023 Coherent Soft Imitation Learning Joe Watson, Sandy Huang, Nicolas Heess

CoRL 2023 Language to Rewards for Robotic Skill Synthesis Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia

ICLR 2023 Lossless Adaptation of Pretrained Vision Models for Robotic Manipulation Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar

AISTATS 2023 Representation Learning in Deep RL via Discrete Information Bottleneck Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb

TMLR 2023 SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin Riedmiller

ICLR 2023 Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio

ICLRW 2023 Towards a Unified Agent with Foundation Models Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin Riedmiller

TMLR 2022 A Generalist Agent Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

JMLR 2022 Behavior Priors for Efficient Reinforcement Learning Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

ICLR 2022 COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation Jongmin Lee, Cosmin Paduraru, Daniel J Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez

NeurIPS 2022 Data Augmentation for Efficient Learning from Parametric Experts Alexandre Galashov, Josh S Merel, Nicolas Heess

ICLR 2022 Evaluating Model-Based Planning and Planner Amortization for Continuous Control Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller

CoLLAs 2022 Forgetting and Imbalance in Robot Lifelong Learning with Off-Policy Data Wenxuan Zhou, Steven Bohez, Jan Humplik, Nicolas Heess, Abbas Abdolmaleki, Dushyant Rao, Markus Wulfmeier, Tuomas Haarnoja

ICLR 2022 Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

CoLLAs 2022 MO2: Model-Based Offline Options Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin Riedmiller, Raia Hadsell, Dushyant Rao

ICLR 2022 NeuPL: Neural Population Learning Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel

ICML 2022 Retrieval-Augmented Reinforcement Learning Anirudh Goyal, Abram Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C Humphreys, Ksenia Konyushova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell

ICML 2022 Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-Sum Games Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess

CoRL 2021 A Constrained Multi-Objective Reinforcement Learning Framework Sandy Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin Riedmiller, Raia Hadsell

CoRL 2021 Collect & Infer - A Fresh Look at Data-Efficient Reinforcement Learning Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess

ICML 2021 Counterfactual Credit Assignment in Model-Free Reinforcement Learning Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos

ICML 2021 Data-Efficient Hindsight Off-Policy Option Learning Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller

NeurIPS 2021 Entropic Desired Dynamics for Intrinsic Control Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih

JAIR 2021 Game Plan: What AI Can Do for Football, and What Football Can Do for AI Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis

NeurIPSW 2021 Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

NeurIPS 2021 Neural Production Systems Anirudh Goyal ALIAS PARTH Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

CoRL 2021 Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan, Noah Yamamoto Siegel, Saran Tunyasuvunakool, Federico Casarini, Nathan Batchelor, Francesco Romano, Stefano Saliceti, Martin Riedmiller, S. M. Ali Eslami, Nicolas Heess

ICML 2020 A Distributional View on Multi-Objective Policy Optimization Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller

ICLR 2020 A Generalized Training Approach for Multiagent Learning Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos

AISTATS 2020 Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions Lars Buesing, Nicolas Heess, Theophane Weber

ICML 2020 CoMic: Complementary Task Learning & Mimicry for Reusable Skills Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel

NeurIPS 2020 Critic Regularized Regression Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

NeurIPS 2020 Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces Guy Lorberbom, Chris J Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

ICLR 2020 Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller

CoRL 2020 Learning Dexterous Manipulation from Suboptimal Experts Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Dan Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori

NeurIPS 2020 RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S Merel, Daniel J Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

ICML 2020 Stabilizing Transformers for Reinforcement Learning Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew Botvinick, Nicolas Heess, Raia Hadsell

CoRL 2020 Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion Roland Hafner, Tim Hertweck, Philipp Kloeppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin Riedmiller

ICLR 2020 V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M. Botvinick

NeurIPS 2020 Value-Driven Hindsight Modelling Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

ICML 2019 Composing Entropic Policies Using Divergence Correction Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess

CoRL 2019 Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

AISTATS 2019 Credit Assignment Techniques in Stochastic Computation Graphs Théophane Weber, Nicolas Heess, Lars Buesing, David Silver

ICLR 2019 Emergent Coordination Through Competition Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

ICLR 2019 Hierarchical Visuomotor Control of Humanoids Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne

NeurIPS 2019 Hindsight Credit Assignment Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Remi Munos

CoRL 2019 Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

ICLR 2019 Information Asymmetry in KL-Regularized RL Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

ICLR 2019 Neural Probabilistic Motor Primitives for Humanoid Control Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

ICLR 2019 Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli

AISTATS 2019 The Termination Critic Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

ICLR 2019 Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau

ICLR 2018 Distributed Distributional Deterministic Policy Gradients Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, Timothy Lillicrap

ICML 2018 Graph Networks as Learnable Physics Engines for Inference and Control Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

ICLR 2018 Learning an Embedding Space for Transferable Robot Skills Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

ICML 2018 Learning by Playing Solving Sparse Reward Tasks from Scratch Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg

ICLR 2018 Maximum a Posteriori Policy Optimisation Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

ICML 2018 Mix & Match Agent Curricula for Reinforcement Learning Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

NeurIPS 2017 Distral: Robust Multitask Reinforcement Learning Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu

ICML 2017 FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

NeurIPS 2017 Filtering Variational Objectives Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh

NeurIPS 2017 Imagination-Augmented Agents for Deep Reinforcement Learning Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra

NeurIPS 2017 Learning Hierarchical Information Flow with Recurrent Neural Modules Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess

ICLR 2017 Metacontrol for Adaptive Imagination-Based Optimization Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia

ICLR 2017 Particle Value Functions Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

NeurIPS 2017 Robust Imitation of Diverse Behaviors Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess

ICLR 2017 Sample Efficient Actor-Critic with Experience Replay Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas

CoRL 2017 Sim-to-Real Robot Learning from Pixels with Progressive Nets Andrei A. Rusu, Matej Vecerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell

NeurIPS 2016 Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton

ICLR 2016 Continuous Control with Deep Reinforcement Learning Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra

NeurIPS 2016 Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess

NeurIPS 2015 Gradient Estimation Using Stochastic Computation Graphs John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

UAI 2015 Kernel-Based Just-in-Time Learning for Passing Expectation Propagation Messages Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

NeurIPS 2015 Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa

NeurIPS 2014 Bayes-Adaptive Simulation-Based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan

ICML 2014 Deterministic Policy Gradient Algorithms David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller

NeurIPS 2014 Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

AISTATS 2014 Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess

NeurIPS 2013 Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn

NeurIPS 2012 Searching for Objects Driven by Context Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari

CVPR 2012 The Shape Boltzmann Machine: A Strong Model of Object Shape S. M. Ali Eslami, Nicolas Heess, John M. Winn